Estimation of the prediction error correlation model in Bayesian model updating

Simoen, Ellen; Papadimitriou, Costas; Lombaert, Geert

doi:10.1201/b16387-286

Cited by 2 publications

(1 citation statement)

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“…Instead, it is typically assumed that the prediction error is Gaussian white noise, that is, uncorrelated with zero mean (Lye et al, 2021). When using closely spaced measurements and model predictions, for example, in the case of time series with high sampling rates or spatial data from densely spaced sensors, dependencies may be present in the model prediction errors (Simoen et al, 1998). The strength of the correlation typically depends on the proximity of the measurements in time and the spacing of sensors on the structure.…”

Section: Problem Statementmentioning

confidence: 99%

Bayesian system identification for structures considering spatial and temporal correlation

Koune,

Rózsás,

Slobbe

et al. 2023

DCE

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The decreasing cost and improved sensor and monitoring system technology (e.g., fiber optics and strain gauges) have led to more measurements in close proximity to each other. When using such spatially dense measurement data in Bayesian system identification strategies, the correlation in the model prediction error can become significant. The widely adopted assumption of uncorrelated Gaussian error may lead to inaccurate parameter estimation and overconfident predictions, which may lead to suboptimal decisions. This article addresses the challenges of performing Bayesian system identification for structures when large datasets are used, considering both spatial and temporal dependencies in the model uncertainty. We present an approach to efficiently evaluate the log-likelihood function, and we utilize nested sampling to compute the evidence for Bayesian model selection. The approach is first demonstrated on a synthetic case and then applied to a (measured) real-world steel bridge. The results show that the assumption of dependence in the model prediction uncertainties is decisively supported by the data. The proposed developments enable the use of large datasets and accounting for the dependency when performing Bayesian system identification, even when a relatively large number of uncertain parameters is inferred.

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Section: Problem Statementmentioning

confidence: 99%